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» Boosting Methods for Regression
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JMLR
2010
153views more  JMLR 2010»
14 years 9 months ago
Feature Extraction for Outlier Detection in High-Dimensional Spaces
This work addresses the problem of feature extraction for boosting the performance of outlier detectors in high-dimensional spaces. Recent years have observed the prominence of mu...
Nguyen Hoang Vu, Vivekanand Gopalkrishnan
187
Voted
ECAI
2008
Springer
15 years 4 months ago
MTForest: Ensemble Decision Trees based on Multi-Task Learning
Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefre...
Qing Wang, Liang Zhang, Mingmin Chi, Jiankui Guo
127
Voted
JMLR
2006
132views more  JMLR 2006»
15 years 2 months ago
Learning to Detect and Classify Malicious Executables in the Wild
We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
Jeremy Z. Kolter, Marcus A. Maloof
135
Voted
ICPR
2008
IEEE
16 years 3 months ago
Shot boundary detection using co-occurrence of global motion in video stream
We propose a method of shot boundary detection based on the co-occurrence of global motion in video stream. In addition to the conventional features based on appearance and local ...
Hironobu Fujiyoshi, Yosuke Murai
136
Voted
MCS
2009
Springer
15 years 9 months ago
Improved Uniformity Enforcement in Stochastic Discrimination
There are a variety of methods for inducing predictive systems from observed data. Many of these methods fall into the field of study of machine learning. Some of the most effec...
Matthew Prior, Terry Windeatt